We constantly hear: “SEO is dead.” or “AI will annihilate organic traffic.” I didn’t want to rely on rumors. I wanted data.

My goal was an experiment under real conditions: Can I precisely target the Google AI Overviews (AIOs) with a new workflow and the right tools, not eventually, but immediately? The focus was on the application of Answer Engine Optimization (AEO) and AI-SEO: AEO refers to the targeted optimization of content to be cited as a source in AI systems like ChatGPT, Perplexity, or Google AI Overview. AI-SEO includes adapting content for AI-based search systems aiming to be visible in generated responses.

Classical SEO strategies are no longer sufficient in the new era of online search. Businesses need to extend their search engine optimization with new approaches like Generative Engine Optimization (GEO) and Engine Optimization to remain present in AI-supported search results and systems.

Search behavior is fundamentally changing: Zero-click searches are increasing as users receive answers directly on the search interface without clicking on traditional websites. The introduction of AI Overviews, Perplexity, Gemini, AI chatbots, AI tools, and large language models (AI models) leads to a significant decline in organic traffic and a decreasing click-through rate (CTR) for traditional search results. Studies show that the introduction of AI Overviews can significantly lower the click rate. These systems integrate artificial intelligence and various technologies to capture, evaluate, and provide relevant content as direct answers. These systems' integration changes the nature of search results and requires businesses to strategically adapt their content.

To be visible in AI-generated answers, content must be clearly structured, precise, and easily comprehensible. The targeted use of long-tail keywords, structured data, a well-thought-out organization, timeliness, and transparent sourcing are crucial. Reading time, presentation style, and compliance with cookie settings also play a role in optimally appealing to both humans and AI systems. A comprehensive approach covering all relevant aspects of a topic increases the chance of being cited as a contribution in AI responses.

Content that appears in AI responses strengthens brand presence and authority in the long term, even if direct traffic declines. Companies need to specifically adjust their content strategy and media presence, create guides and contributions for various media channels, and consider the integration of AI tools into their systems to remain visible in the age of artificial intelligence.

I conducted the test in the niche of “Revenue Marketing”—a fiercely competitive B2B topic typically dominated by US giants.

The log of my 5-day experiment (15.01. to 20.01.2026) proves: The rules have changed. Those who know them, win.

The Strategy: Applying the "iGrow Framework"

The mistake most make: They start with a keyword tool and look for high volume. I approached it differently. I wanted to know: What are the logical follow-up opportunities of a topic? In the era of AI search engines, new SEO strategies are needed that specifically address how AI systems work. Especially the integration of long-tail keywords is crucial as they increase the content's relevance for AI models and cover broader and more detailed search intentions.

I relied on the concept of Fan-Out Queries. Studies show that ranking for the main term only brings < 20% of AI citations. The real action happens in subqueries (Fan-Outs). A clear structure and using structured data like schema.org markup, FAQ pages, or lists further enhance the discoverability and processing of content by AI tools. While traditional SEO measures remain important, they must be supplemented by AI-specific strategies to stay visible in AI-generated answers and new search results.

Step 1: The Prompt Analysis (15.01.2026)

Instead of blindly optimizing “Revenue Marketing”, I used the AI tool RankScale.ai to identify content gaps for visibility and citability in AI search engines. AI tools play a central role in optimizing texts for AI-SEO, Answer Engine Optimization, and Generative Engine Optimization, as they help analyze relevant topics and search behaviors and strategically adapt content.

AI systems and models like ChatGPT evaluate content for depth, accuracy, and contextual relevance. Especially current information, transparent sourcing, and clearly structured content—such as modular sections, FAQ pages, or lists—increase the likelihood of being cited in AI search results. Therefore, companies should combine traditional SEO measures with new strategies for visibility in AI and AI Overviews to optimally position their brand and content.

I searched for prompts that AI still had few perfect, structured answers for. RankScale provided me with two specific search terms with high “AI potential”:

1. The Comparison:“What is the difference between traditional marketing and revenue marketing?“

2. The Trend:“Revenue Marketing Trends 2026“

Step 2: Content Creation ("Writing for Machines")

Based on these prompts, I wrote two articles. I didn’t follow a classic “Wall of Text” structure but an answer structure. A clear outline, modular sections, and logically structured content are crucial for AI systems like ChatGPT, AI chatbots, and generative AI models to efficiently process and use the content as a source. Content should be precise, easy to grasp, and divided into self-contained sections since AIs analyze texts in modular units. Integrating structured data like FAQ pages, lists, or schema.org markup significantly improves the findability and processing of content by AI tools and search engines. Most importantly, the content is written in clear, simple, and natural language to optimally appeal to both humans and AI systems and increase readability.

• For the comparison article: I used H2 headlines as questions. Below, clear bullet points followed that directly contrasted “Traditional” and “Revenue”. I avoided text blocks where facts were needed.

• For the trend article: I provided a numbered list with specific forecasts for 2026. I used signal words like “Forecast”, “Development”, and year numbers.

My goal was to serve the information to the AI as “bite-sized” as possible.

Phase 1: The "Immediate Effect" after 22 Hours (Mobile AIO)

What happened next surprised even me in its speed.

Only 22 hours after publication (around 16.01.), I checked the results on the smartphone, where Google plays out the AI Overviews most aggressively.

The results were clear and reproducible:

1. Citations in AI Overview: Google generated an "AI Overview" box at the top for both search queries. For the questions about "Difference" or "Trends", both articles were cited as primary sources. The AI copied my bullet points almost 1:1 in its answer box.
   
   

2. Organic Dominance: Both articles simultaneously ranked directly below the AI box at number 1 in organic search. I thus took over the entire screen (“Above the Fold”).
   
   

3. The International Phenomenon (Cross-Language): Particularly exciting was the trend article. I searched in English for "Revenue Marketing Trends".
   
   

   • The result: The English AI Overview cited my German article (igrow.at).

   • The insight: The AI recognized the relevance of my data (“Trends 2026”), ignored the language barrier, and translated the facts on-the-fly. Authority beats language.

Phase 2: The Under-the-Hood Look (Technical Validation)

Visual results are good, but I wanted to ensure that this wasn’t just a random hit. I went back to RankScale to check if the tool had technically registered this “hit”.

The data under “Execution Details” confirmed the picture scientifically:

• The Engine: RankScale displayed that the “Google AI Mode GUI” engine had processed my content.

• The Citation: In the list of “Text Citations”, my URL (…/was-ist-der-unterschied…) appeared at position 7.

• The Score: The Visibility Score on the dashboard shot up to 45.5%.

This means: The tool correctly identified the gap, I filled it, and the Google engine technically indexed the content and marked it as a source. For visibility in AI-generated answers, it is crucial to understand the system's workings—from web crawlers through indexing and answer generation systems to language processing models—and to engage in targeted Engine Optimization. Optimizing content for AI-based search systems requires new strategies and a deep understanding of how AI search engines evaluate content and select it as a source in AI Overviews or generative search results.

Phase 3: The "Stress Test" on Desktop (20.01.2026)

Many SEO hacks work only briefly or only on mobile. I wanted to see: Does the strategy hold after 5 days and on desktop?

With the introduction of the Search Generative Experience, a new search experience through the integration of generative AI models into Google Search, the search behavior fundamentally changes. Users frequently receive complete answers directly in the search results through these AI tools and systems without having to click on a website. This impacts expectations of search engines and presents new challenges for search engine optimization (SEO), particularly regarding visibility in AI, content optimization, and the role as a source in AI Overviews.

On January 20th, 2026, I conducted the self-test on a desktop PC in full Google AI Mode.

Test A: The Comparison in AI Mode

I entered the exact prompt: “what is the difference between revenue marketing vs. traditional marketing?”

• The Result: Google provided a precise AI summary at the top (“Focus on Brand Awareness” vs. “Revenue”). Here, AI models such as large language models (LLMs) rely on structured content, as these are evaluated by the systems for depth, accuracy, and contextual relevance. AI chatbots and AI tools integrate this information to generate relevant answers for the changed search behavior in online search. For search engine optimization (SEO), this means: Artificial intelligence prefers clearly structured, context-rich articles as sources for AI Overviews and thus increases visibility in AI search results.
  
  

• The Dominance: Directly below, my article ranked on number 1 organically. The user cannot bypass my brand.

Test B: The Conversational Deep Dive (Chat)

Then I switched to the interactive chat mode (Conversational Search) to see if I would also be cited in follow-up questions. Here, users seek depth. AI chatbots and other AI-driven systems play a central role in understanding and answering user queries and frequently pulling web content in real-time. Importantly, AIs prefer content written in a natural, conversational language, as this eases integration into the answers of AI chatbots.

• The Result: The AI broke down the topic into detailed points (“Objective and KPIs”, “Collaboration”).

• The Citation: In the source panel on the right (“14 Websites”), my article appeared as the first image card on top.

• Why? Because my article provided exactly this structure. The AI used my H2 structure to build its answer. I was the architect of its answer.

Test C: The Sidebar Trends

The same picture emerged on January 20th for the trend topic on desktop: My article on “Trends 2026” was prominently featured in the right sidebar. The “Freshness” of the content (2026) was the key here against outdated competitor articles.

For visibility in AI tools and AI Overviews, the currency of content freshness and the transparent provision of sources are crucial. Regularly updating articles and integrating clear source mentions increases chances of better integration into online search and search engine optimization (SEO) through artificial intelligence and new search behaviors at Google.

Measuring AI Visibility: How Does Success Become Visible?

AI rankings are a must. Classic SERP positions are no longer sufficient. Visibility in AI systems like Google AI Overviews, Bing Copilot, or ChatGPT decides whether content is noticed at all. Measuring this visibility is operationally necessary.

1. New Metrics for the AI Age

SERP positions were yesterday. Today, specific citations in AI-generated answers count.

In practice, this means:

• Source Mention Rate: How often is the domain referenced as a source?

• Brand Mention in AI Answers: Measurable presence in answer boxes

• LLM Citation: Demonstrable referencing in Large Language Models

Goal: AI visibility becomes the primary currency for companies. Not optional.

2. Tools and Methods to Measure AI Visibility

Analysis: Why Did It Work?

In these 5 days, I didn’t “trick” the algorithm. I just gave it exactly what it desperately seeks.

1. Prompts > Keywords: I didn’t write for the keyword “Marketing”. I answered a specific question (prompt) that RankScale identified as a gap.
   
   

2. Structure is the Currency: Being cited both on mobile and in the desktop chat is due to the structure (H2s as questions, clear lists). The AI reads HTML, not prose. Optimizing for AI search engines requires a clear structure and precise formulations so systems like ChatGPT, Perplexity, or Gemini can recognize and cite content as a source.
   
   

3. Omnichannel Success: The strategy works across platforms. Whoever is the “Source of Truth” for the AI wins in AI Overview (Mobile) and AI Mode (Desktop).
   
   

Answer Engine Optimization (AEO) is the new approach to specifically optimizing content for AI-based answer tools. Visibility in AI-generated answers requires new strategies and an in-depth understanding of how AI models and generative search engines evaluate and select content. Traditional SEO measures are no longer enough—businesses must strategically adapt their content to appear as a credible source in the answers of AI systems in the age of Artificial Intelligence.

My Conclusion: We need to stop writing texts for search engines of 2020. Whoever delivers answers today that AI can process becomes the source. And whoever becomes the source wins the traffic. If you want to know more about AI-search optimization or conduct a non-binding Growth Audit with me, I cordially invite you.

To good citations, Edin – Author & CEO of igrow.at
P.S. I look forward to connecting with you on LinkedIn!

FAQ: Questions about AI Optimization

Here are the answers to the questions I received since the screenshots were published. For businesses looking to optimize their content for AI search engines, we offer practical tips and a guide to successfully integrate AI-SEO strategies and Answer Engine Optimization (AEO). These help specifically increase visibility in AI-based answer systems.